🌟 500+ ai agent projects / usecases

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a curated collection of ai agent use cases across industries, showcasing practical applications and linking to open-source projects for implementation. explore how ai agents are transforming industries like healthcare, finance, education, and more! 🤖✨

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📋 table of contents

• introduction
• industry usecase
• use case table
• framework wise usecase
  • crewai usecase
  • autogen usecase
  • agno usecase
  • langgraph usecase
• contributing
• license

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🧠 introduction

artificial intelligence (ai) agents are revolutionizing the way industries operate. from personalized learning to financial trading bots, ai agents bring efficiency, innovation, and scalability. this repository provides:

• a categorized list of industries where ai agents are making an impact.
• detailed use cases with links to open-source projects for implementation.

whether you're a developer, researcher, or business enthusiast, this repository is your go-to resource for ai agent inspiration and learning.

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🏭 industry usecase mindmap

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🧩 use case table

| use case | industry | description | code github | | ------------------------------------------- | ---------------- | -------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | hia (health insights agent) | healthcare | analyses medical reports and provide health insights. | | | ai health assistant | healthcare | diagnoses and monitors diseases using patient data. | | | automated trading bot | finance | automates stock trading with real-time market analysis. | | | virtual ai tutor | education | provides personalized education tailored to users. | | | 24/7 ai chatbot | customer service | handles customer queries around the clock. | | | product recommendation agent | retail | suggests products based on user preferences and history. | | | self-driving delivery agent | transportation | optimizes routes and autonomously delivers packages. | | | factory process monitoring agent | manufacturing | monitors production lines and ensures quality control. | | | property pricing agent | real estate | analyzes market trends to determine property prices. | | | smart farming assistant | agriculture | provides insights on crop health and yield predictions. | | | energy demand forecasting agent | energy | predicts energy usage to optimize grid management. | | | content personalization agent | entertainment | recommends personalized media based on preferences. | | | legal document review assistant | legal | automates document review and highlights key clauses. | | | recruitment recommendation agent | human resources | suggests best-fit candidates for job openings. | | | virtual travel assistant | hospitality | plans travel itineraries based on preferences. | | | ai game companion agent | gaming | enhances player experience with real-time assistance. | | | real-time threat detection agent | cybersecurity | identifies potential threats and mitigates attacks. | | | e-commerce personal shopper agent | e-commerce | helps customers find products they’ll love. | | | logistics optimization agent | supply chain | plans efficient delivery routes and manages inventory. | |

framework wise usecases

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framework name: crewai

| use case | industry | description | github | | -------------------------------- | ----------------------- | -------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | | 📧 email auto responder flow | 🗣️ communication | automates email responses based on predefined criteria to enhance communication efficiency. | | | 📝 meeting assistant flow | 🛠️ productivity | assists in organizing and managing meetings, including scheduling and agenda preparation. | | | 🔄 self evaluation loop flow | 👥 human resources | facilitates self-assessment processes within an organization, aiding in performance reviews. | | | 📈 lead score flow | 💼 sales | evaluates and scores potential leads to prioritize outreach in sales strategies. | | | 📊 marketing strategy generator | 📢 marketing | develops marketing strategies by analyzing market trends and audience data. | | | 📝 job posting generator | 🧑‍💼 recruitment | creates job postings by analyzing job requirements, aiding in recruitment processes. | | | 🔄 recruitment workflow | 🧑‍💼 recruitment | streamlines the recruitment process by automating various tasks involved in hiring. | | | 🔍 match profile to positions | 🧑‍💼 recruitment | matches candidate profiles to suitable job positions to enhance recruitment efficiency. | | | 📸 instagram post generator | 📱 social media | generates and schedules instagram posts automatically, streamlining social media management. | | | 🌐 landing page generator | 💻 web development | automates the creation of landing pages for websites, facilitating web development tasks. | | | 🎮 game builder crew | 🎮 game development | assists in the development of games by automating certain aspects of game creation. | | | 💹 stock analysis tool | 💰 finance | provides tools for analyzing stock market data to assist in financial decision-making. | | | 🗺️ trip planner | ✈️ travel | assists in planning trips by organizing itineraries and managing travel details. | | | 🎁 surprise trip planner | ✈️ travel | plans surprise trips by selecting destinations and activities based on user preferences. | | | 📚 write a book with flows | ✍️ creative writing | assists authors in writing books by providing structured workflows and writing assistance. | | | 🎬 screenplay writer | ✍️ creative writing | aids in writing screenplays by offering templates and guidance for script development. | | | ✅ markdown validator | 📄 documentation | validates markdown files to ensure proper formatting and adherence to standards. | | | 🧠 meta quest knowledge | 📚 knowledge management | manages and organizes knowledge related to meta quest, facilitating information retrieval. | | | 🤖 nvidia models integration | 🤖 ai integration | integrates nvidia ai models into workflows to enhance computational capabilities. | | | 🗂️ prep for a meeting | 🛠️ productivity | assists in preparing for meetings by organizing materials and setting agendas. | | | 🛠️starter template | 🛠️ development | provides a starter template for new projects to streamline the setup process. | | | 🔗crewai + langgraph integration | 🤖 ai integration | demonstrates integration between crewai and langgraph for enhanced workflow automation. | |

framework name: autogen

code generation, execution, and debugging

| use case | industry | description | notebook | | --------------------------------------------------------------------------------------- | ----------------------- | --------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🤖 automated task solving with code generation, execution & debugging | 💻 software development | demonstrates automated task-solving by generating, executing, and debugging code. | | | 🧑‍💻 automated code generation and question answering with retrieval augmented agents | 💻 software development | generates code and answers questions using retrieval-augmented methods. | | | 🧠 automated code generation and question answering with qdrant-based retrieval | 💻 software development | utilizes qdrant for enhanced retrieval-augmented agent performance. | |

multi-agent collaboration (>3 agents)

| use case | industry | description | notebook | | :----------------------------------------------------------------------- | :-------------------------- | :------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | 🤝 automated task solving by group chat (3 members, 1 manager) | 🤝 collaboration | demonstrates group task-solving via multi-agent collaboration. | | | 📊 automated data visualization by group chat (3 members, 1 manager) | 📊 data analysis | uses multi-agent collaboration to create data visualizations. | | | 🧩 automated complex task solving by group chat (6 members, 1 manager) | 🤝 collaboration | solves complex tasks collaboratively with a larger group of agents. | | | 🧑‍💻 automated task solving with coding & planning agents | 🛠️ planning & development | combines coding and planning agents for solving tasks effectively. | | | 📐 automated task solving with transition paths specified in a graph | 🤝 collaboration | uses predefined transition paths in a graph for solving tasks. | | | 🧠 running a group chat as an inner-monologue via the societyofmindagent | 🧠 cognitive sciences | simulates inner-monologue for problem-solving using group chats. | | | 🔧 running a group chat with custom speaker selection function | 🤝 collaboration | implements a custom function for speaker selection in group chats. | |

sequential multi-agent chats

| use case | industry | description | notebook | | :--------------------------------------------------------------------------------- | :--------------------- | :------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🔄 solving multiple tasks in a sequence of chats initiated by a single agent | 🔄 workflow automation | automates sequential task-solving with a single initiating agent. | | | ⏳ async-solving multiple tasks in a sequence of chats initiated by a single agent | 🔄 workflow automation | handles asynchronous task-solving in a sequence of chats initiated by one agent. | | | 🤝 solving multiple tasks in a sequence of chats initiated by different agents | 🔄 workflow automation | facilitates sequential task-solving with different agents initiating each chat. | |

nested chats

| use case | industry | description | notebook | | :----------------------------------------------------------------------------- | :--------------------------- | :------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🧠 solving complex tasks with nested chats | 🧠 problem solving | uses nested chats to solve hierarchical and complex problems. | | | 🔄 solving complex tasks with a sequence of nested chats | 🧠 problem solving | demonstrates sequential task-solving using nested chats. | | | 🏭 optiguide for solving a supply chain optimization problem with nested chats | 🏭 supply chain optimization | showcases how to solve supply chain optimization problems using nested chats, a coding agent, and a safeguard agent. | | | ♟️ conversational chess with nested chats and tool use | 🎮 gaming | explores the use of nested chats for playing conversational chess with integrated tools. | |

application

| use case | industry | description | notebook | | :------------------------------------------------------------------------------------------------- | :--------------------------- | :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------ | | 🔄 automated continual learning from new data | 📊 machine learning | continuously learns from new data inputs for adaptive ai. | | | 🏭 optiguide - coding, tool using, safeguarding & question answering for supply chain optimization | 🏭 supply chain optimization | highlights a solution combining coding, tool use, and safeguarding for supply chain optimization. | | | 🤖 autoanny - a discord bot built using autogen | 💬 communication tools | showcases the development of a discord bot using autogen for enhanced interaction. | |

tools

| use case | industry | description | notebook | | :--------------------------------------------------------------------- | :----------------------------- | :------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🌐 web search: solve tasks requiring web info | 🔍 information retrieval | searches the web to gather information required for completing tasks. | | | 🔧 use provided tools as functions | 🛠️ tool integration | demonstrates how to use pre-provided tools as callable functions in autogen. | | | 🔗 use tools via sync and async function calling | 🛠️ tool integration | illustrates synchronous and asynchronous tool usage within autogen workflows. | | | 🧩 task solving with langchain provided tools as functions | 🔍 language processing | leverages langchain tools for task-solving within autogen. | | | 📚 rag: group chat with retrieval augmented generation | 🤝 collaboration | enables group chat with retrieval augmented generation (rag) to support information sharing. | | | ⚙️ function inception: update/remove functions during conversations | 🔧 development tools | allows autogen agents to modify their functions dynamically during conversations. | | | 🔊 agent chat with whisper | 🎙️ audio processing | demonstrates ai agent capabilities for transcription and translation using whisper. | | | 📏 constrained responses via guidance | 💡 natural language processing | shows how to use guidance to constrain responses generated by agents. | | | 🌍 browse the web with agents | 🌐 information retrieval | explains how to configure agents to browse and retrieve information from the web. | | | 📊 sql: natural language text to sql query using spider benchmark | 💾 database management | converts natural language inputs into sql queries using the spider benchmark. | | | 🕸️ web scraping with apify | 🌐 data gathering | illustrates web scraping techniques with apify using autogen. | | | 🕷️ web crawling: crawl entire domain with spider api | 🌐 data gathering | explains how to crawl entire domains using the spider api. | | | 💻 write a software app task by task with specially designed functions | 💻 software development | builds a software application step-by-step using designed functions. | |

human development

| use case | industry | description | notebook | | :--------------------------------------------------------------- | :---------------------- | :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------ | | 💬 simple example in chatgpt style | 🧠 conversational ai | demonstrates a simple conversational example in the style of chatgpt. | | | 🤖 auto code generation, execution, debugging and human feedback | 💻 software development | showcases code generation, execution, debugging with human feedback integrated into the workflow. | | | 👥 automated task solving with gpt-4 + multiple human users | 🤝 collaboration | enables task solving with multiple human users collaborating with gpt-4. | | | 🔄 agent chat with async human inputs | 🧠 conversational ai | supports asynchronous human input during agent conversations. | |

agent teaching and learning

| use case | industry | description | notebook | | :------------------------------------------------------------------- | :-------------------------- | :--------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 📘 teach agents new skills & reuse via automated chat | 🎓 education & training | demonstrates teaching new skills to agents and enabling their reuse in automated chats. | | | 🧠 teach agents new facts, user preferences and skills beyond coding | 🎓 education & training | shows how to teach agents new facts, user preferences, and non-coding skills. | | | 🤖 teach openai assistants through gptassistantagent | 💻 ai assistant development | illustrates how to enhance openai assistants' capabilities using gptassistantagent. | | | 🔄 agent optimizer: train agents in an agentic way | 🛠️ optimization | explains how to train agents effectively in an agentic manner using the agent optimizer. | |

multi-agent chat with openai assistants in the loop

| use case | industry | description | notebook | | :-------------------------------------------------------- | :----------------------- | :---------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🌟 hello-world chat with openai assistant in autogen | 🤖 conversational ai | a basic example of chatting with openai assistant using autogen. | | | 🔧 chat with openai assistant using function call | 🔧 development tools | illustrates how to use function calls with openai assistant in chats. | | | 🧠 chat with openai assistant with code interpreter | 💻 software development | demonstrates the use of openai assistant as a code interpreter in chats. | | | 🔍 chat with openai assistant with retrieval augmentation | 📚 information retrieval | enables retrieval-augmented conversations with openai assistant. | | | 🤝 openai assistant in a group chat | 🤝 collaboration | shows how openai assistant can collaborate with other agents in a group chat. | | | 🛠️ gptassistantagent based multi-agent tool use | 🔧 development tools | explains how to use gptassistantagent for multi-agent tool usage. | |

non-openai models

| use case | industry | description | notebook | | :------------------------------------------------ | :-------- | :---------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ♟️ conversational chess using non-openai models | 🎮 gaming | explores conversational chess implemented with non-openai models. | |

multimodal agent

| use case | industry | description | notebook | | :--------------------------------------------- | :------------------ | :-------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🎨 multimodal agent chat with dalle and gpt-4v | 🖼️ multimedia ai | combines dalle and gpt-4v for multimodal agent communication. | | | 🖌️ multimodal agent chat with llava | 📷 image processing | uses llava for enabling multimodal agent conversations with image processing. | | | 🖼️ multimodal agent chat with gpt-4v | 🖼️ multimedia ai | leverages gpt-4v for visual and conversational interactions in multimodal agents. | |

long context handling

| use case | industry | description | notebook | | :--------------------------------------- | :--------------- | :--------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------- | | 📜 long context handling as a capability | 🧠 ai capability | demonstrates techniques for handling long context effectively within ai workflows. | |

evaluation and assessment

| use case | industry | description | notebook | | :----------------------------------------------------------------------------------- | :------------------------ | :------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------- | | 📊 agenteval: a multi-agent system for assessing utility of llm-powered applications | 📈 performance evaluation | introduces agenteval for evaluating and assessing the performance of llm-based applications. | |

automatic agent building

| use case | industry | description | notebook | | :------------------------------------------------------------ | :---------------- | :------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🏗️ automatically build multi-agent system with agentbuilder | 🤖 ai development | explains how to automatically build multi-agent systems using the agentbuilder tool. | | | 📚 automatically build multi-agent system from agent library | 🤖 ai development | shows how to construct multi-agent systems by leveraging a pre-defined agent library. | |

observability

| use case | industry | description | notebook | | :---------------------------------------------------------------- | :------------------------ | :----------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------ | | 📊 track llm calls, tool usage, actions and errors using agentops | 📈 monitoring & analytics | demonstrates how to monitor llm interactions, tool usage, and errors using agentops. | |

enhanced inferences

| use case | industry | description | notebook | | :--------------------------------------------------------------------- | :----------------- | :----------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🔗 api unification | 🔧 api management | explains how to unify api usage with documentation and code examples. | | | ⚙️ utility functions to help managing api configurations effectively | 🔧 api management | demonstrates utility functions to manage api configurations more effectively. | | | 💰 cost calculation | 📈 cost management | introduces methods for tracking token usage and estimating costs for llm interactions. | | | ⚡ optimize for code generation | 📊 optimization | highlights cost-effective optimization techniques for improving code generation with llms. | | | 📐 optimize for math | 📊 optimization | explains techniques to optimize llm performance for solving mathematical problems. | |

framework name: agno

usecase

| use case | industry | description | notebook | | :--------------------------------- | :----------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🤖 support agent | 💻 software development / ai / framework support | the agno support agent helps developers with the agno framework by providing real-time answers, explanations, and code examples. | | | 🎥 youtube agent | 📺 media & content | an intelligent agent that analyzes youtube videos by generating detailed summaries, timestamps, themes, and content breakdowns using ai tools. | | | 📊 finance agent | 💼 finance | an advanced ai-powered market analyst that delivers real-time stock market insights, analyst recommendations, financial deep-dives, and sector-specific trends. supports prompts for detailed analysis of companies like aapl, tsla, nvda, etc. | | | 📚 study partner | 🎓 education | assists users in learning by finding resources, answering questions, and creating study plans. | | | 🛍️ shopping partner agent | 🏬 e-commerce | a product recommender agent that helps users find matching products based on preferences from trusted platforms like amazon, flipkart, etc. | | | 🎓 research scholar agent | 🧠 education / research | an ai-powered academic assistant that performs advanced academic searches, analyzes recent publications, synthesizes findings across disciplines, and writes well-structured academic reports with proper citations. | | | 🧠 research agent | 🗞️ media & journalism | a research agent that combines web search and professional journalistic writing. it performs deep investigations and produces nyt-style reports. | | | 🍳 recipe creator | 🍽️ food & culinary | an ai-powered recipe recommendation agent that provides personalized recipes based on ingredients, preferences, and time constraints. | | | 🗞️ finance agent | 💼 finance | a powerful financial analyst agent combining real-time stock data, analyst insights, company fundamentals, and market news. ideal for analyzing companies like apple, tesla, nvidia, and sectors like semiconductors or automotive. | | | 🧠 financial reasoning agent | 📈 finance | uses a claude-3.5 sonnet-based agent to analyze stocks like nvda using tools for reasoning and yahoo finance data. | | | 🤖 readme generator agent | 💻 software dev | agent generates high-quality readmes for github repositories using repo metadata. | | | 🎬 movie recommendation agent | 🎥 entertainment | an intelligent agent that gives personalized movie recommendations using exa and gpt-4o, analyzing genres, themes, and latest ratings. | | | 🔍 media trend analysis agent | 📰 media & news | analyzes emerging trends, patterns, and influencers from digital platforms using ai-powered agents and scraping. | | | ⚖️ legal document analysis agent | 🏛️ legal tech | an ai agent that analyzes legal documents from pdf urls and provides legal insights based on a knowledge base using vector embeddings and gpt-4o. | | | 🤔 deepknowledge | 🧠 research | this agent performs iterative searches through its knowledge base, breaking down complex queries into sub-questions and synthesizing comprehensive answers. it uses agno docs for demonstration and is designed for deep reasoning and exploration. | | | 📚 book recommendation agent | 🧠 publishing & media | an intelligent agent that provides personalized book suggestions using literary data, reader preferences, reviews, and release info. | | | 🏠 mcp airbnb agent | 🛎️ hospitality | create an ai agent using mcp and llama 4 to search airbnb listings with filters like workspace & transport proximity. | | | 🤖 assist agent | 🧠 ai framework | an ai agent using gpt-4o to answer questions about the agno framework with hybrid search and embedded knowledge. | |

framework name: langgraph

usecase

| use case | industry | description | notebook | | :------------------------------------ | :---------------------------- | :----------------------------------------------------------- | :----------------------------------------------------------- | | 🤖 chatbot simulation evaluation | 💻 💬 ai / quality assurance | simulate user interactions to evaluate chatbot performance, ensuring robustness and reliability in real-world scenarios. | | | 🧠 information gathering via prompting | 🧠 ai / research & development | this tutorial demonstrates how to design a langgraph workflow that utilizes prompting techniques to gather information effectively. it showcases how to structure prompts and manage the flow of information to build intelligent agents. | | | 🧠 code assistant with langgraph | 💻 software development | this tutorial demonstrates how to build a resilient code assistant using langgraph. it guides you through creating a graph-based agent that can handle code generation, error checking, and iterative refinement, ensuring robust and accurate coding assistance. | | | 🧑‍💼 customer support agent | 🧑‍💼 customer support agent | this tutorial demonstrates how to build a customer support agent using langgraph. it guides you through creating a graph-based agent that can handle customer inquiries, providing automated support and enhancing user experience. | | | 🔁 extraction with retries | 🧠 ai / data extraction | this tutorial demonstrates how to implement retry mechanisms in langgraph workflows, ensuring robust data extraction processes that can handle transient errors and improve reliability. | | | 🧠 multi-agent workflow | 🧠 ai / workflow orchestration | this tutorial demonstrates how to build a multi-agent system using langgraph's agent supervisor. it guides you through creating a supervisor agent that orchestrates multiple specialized agents, managing task delegation and communication flow. | | | 🧠 hierarchical agent teams | 🧠 ai / workflow orchestration | this tutorial demonstrates how to build a hierarchical agent system using langgraph. it guides you through creating a top-level supervisor agent that delegates tasks to specialized sub-agents, enabling complex workflows with clear task delegation and communication. | | | 🤝 multi-agent collaboration | 🧠 ai / workflow orchestration | this tutorial demonstrates how to implement multi-agent collaboration using langgraph. it guides you through creating multiple specialized agents that work together to accomplish a complex task, showcasing the power of agent collaboration in ai workflows. | | | 🧠 plan-and-execute agent | 🧠 ai / workflow orchestration | this tutorial demonstrates how to build a "plan-and-execute" style agent using langgraph. it guides you through creating an agent that first generates a multi-step plan and then executes each step sequentially, revisiting and modifying the plan as necessary. this approach is inspired by the plan-and-solve paper and the baby-agi project, aiming to enhance long-term planning and task execution in ai workflows. | | | 🧠 sql agent | 🧠 ai / database interaction | this tutorial demonstrates how to build an agent that can answer questions about a sql database. the agent fetches available tables, determines relevance to the question, retrieves schemas, generates a query, checks for errors, executes it, and formulates a response. | | | 🧠 reflection agent | 🧠 ai / workflow orchestration | this tutorial demonstrates how to build a reflection agent using langgraph. it guides you through creating an agent that can critique and revise its own outputs, enhancing the quality and reliability of generated content. | | | 🧠 reflexion agent | 🧠 ai / workflow orchestration | this tutorial demonstrates how to build a reflexion agent using langgraph. it guides you through creating an agent that can reflect on its actions and outcomes, enabling iterative improvement and more accurate decision-making in complex workflows. | | | langgraph agentic rag | | | | | 🧠 adaptive rag | 🧠 ai / information retrieval | this tutorial demonstrates how to build an adaptive rag system using langgraph. it guides you through creating a dynamic retrieval process that adjusts based on query complexity, enhancing the efficiency and accuracy of information retrieval. | | | 🧠 adaptive rag (local) | 🧠 ai / information retrieval | this tutorial focuses on implementing adaptive rag with local models, allowing for offline retrieval and generation, which is crucial for environments with limited internet access or privacy concerns. | | | 🤖 agentic rag | 🤖 ai / intelligent agents | learn to build an agentic rag system where an agent determines the best retrieval strategy before generating a response, improving the relevance and accuracy of answers. | | | 🤖 agentic rag (local) | 🤖 ai / intelligent agents | this tutorial extends agentic rag to local environments, enabling the use of local models and data sources for retrieval and generation tasks. | | | 🧠 corrective rag (crag) | 🧠 ai / information retrieval | implement a corrective rag system that evaluates and refines retrieved documents before passing them to the generator, ensuring higher-quality outputs. | | | 🧠 corrective rag (local) | 🧠 ai / information retrieval | this tutorial focuses on building a corrective rag system using local resources, allowing for offline document evaluation and refinement processes. | | | 🧠 self-rag | 🧠 ai / information retrieval | learn to implement self-rag, where the system reflects on its responses and retrieves additional information if necessary, enhancing the accuracy and relevance of generated content. | | | 🧠 self-rag (local) | 🧠 ai / information retrieval | this tutorial demonstrates how to implement self-rag using local models and data sources, enabling offline reflection and retrieval processes. | |

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🤝 contributing

contributions are welcome! 🎉 here's how you can help:

1. fork the repository.
2. add a new use case or improve an existing one.
3. submit a pull request with your changes.

please follow our contributing guidelines for more details.

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📜 license

this repository is licensed under the mit license. see the license file for more information.

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